A genetic algorithm based procedure for more realistic job shop scheduling problems

This work presents a robust procedure to solve job shop scheduling problems with large number of more realistic constraints such as jobs with several subassembly levels, alternative processing plans for parts and alternative resources for operations, requirement of multiple resources to process an operation (e.g. machine, tools, fixtures, staff), resource calendars, batch overlap and sequence dependent setups. Also, the approach considers multi-objective evaluation functions. The system uses modified schedule generation algorithms to obtain a set of initial solutions. Each initial solution is enhanced by a local improvement procedure. Then a hybrid genetic algorithm, which incorporates a local hill climbing procedure, is applied to the set of local optimum schedules.